Fukuda, MunehiroRakhmatullaev, Akbarbek Azamatovich2026-02-052026-02-052026-02-052025Rakhmatullaev_washington_0250O_29173.pdfhttps://hdl.handle.net/1773/55199Thesis (Master's)--University of Washington, 2025Vector search plays a crucial role in large-scale similarity search applications, with IVF (Inverted File Index) being a widely used indexing method due to its balance between accuracy and efficiency. However, traditional vector search algorithms that used IVF as an indexing method, such as IVF Flat and IVFPQ, yield results by brute force searching within each cluster/list. This paper introduces a new IVF-based vector search algorithm, called IVF Singular Search, which does the search within each cluster/list through a different arrangement of data and traversal using the Binary Search. In order to accelerate the development phase, the author used MASS CUDA to handle the searching part, which allowed to leverage the abstraction level of the code. We evaluated our IVF Singular Search, implemented for GPUs using MASS CUDA, against two other algorithms, IVF Flat and IVFPQ, demonstrating the significant speed efficiency of the approach. The findings suggest IVF Singular Search can make vector search more efficient and robust on infrastructure that requires immediate response, such as navigation systems or robots.application/pdfen-USCC BY-SAAgentsBinary SearchInverted File IndexIVFVectorVector SearchComputer scienceComputer science and engineeringThesis